IAP 2026: Expanding Horizons in Computing

Practical Software Carpentries aims to help researchers get their work done in less time and with less pain by teaching them basic research computing skills. This three-day, hands-on introductory workshop will cover basic concepts and tools. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.

Schedule

• Tuesday, January 13: Introduction to Unix Shell
• Wednesday, January 14: Introduction to Git/GitHub
• Thursday, January 15: Introduction to Python

All workshops take place in person, from 10 am to 4 pm each day. Register for 1, 2, or all 3 days (you must register for each workshop separately). Open to both MIT and non-MIT affiliates. Space is limited.

A four-day introductory course on the role of High Performance Computing (HPC, aka supercomputing) in research. We will discuss the fields where HPC is used and provide concrete examples where we describe the strategies used to scale applications to hundreds of processors. Students will learn when to scale from their laptops to HPC, what challenges that introduces, and how to address those challenges with efficient HPC workflows. Engaging will be used for hands-on examples using C/C++, Julia, Matlab, and/or Python. We will also demonstrate applications using other computing resources on campus, such as the Satori and SuperCloud clusters. Students should bring an existing research problem/application that they would like to scale as a project.

This is a blended course with asynchronous and live components. Much of the lecture is available before class in pre-recorded short videos and class time will be spent on hands-on activities and student research project work.

Schedule & Agenda

• Dates: January 20, 22, 27, 29 (Tuesdays and Thursdays)
• Time: 1:00–4:00 pm (lunch available 12–1 pm)

The class session topics are as follows:

• Day 1: Introduction to Supercomputing Workflows and Systems
• Day 2: Serial Optimization and Parallel Speedup
• Day 3: Building and Running Parallel Workflows
• Day 4: Distributed Computing

While the course was designed as a series, each session could be taken as a stand-alone module.

How to Sign Up

Email the instructor, Lauren Milechin, for the registration link. You must be a current MIT student (undergraduate or graduate), post-doctoral associate, or researcher to take the class.

Space is limited. You must complete the registration form no later than January 12 (before 5 pm). Registration will close earlier if the class reaches capacity before that date.

AI this, AI that — but how can you use AI to manage your own data as a researcher? Join us for a four-day bootcamp to learn how to set up a RAG system and build a chat interface over your data! From how to structure your experimental results for efficient access, to how to preprocess academic papers for retrieval by LLMs, we will demystify all the steps along the way. Introductory (very basic, really) Python knowledge assumed.

This class is part of the Practical Computational Thinking IAP series, taught by students from the Data Systems Group at CSAIL and ORCD.

Schedule & Agenda

• Dates: January 26–29 (Monday–Thursday)
• Time: 10:00 am–12:00 pm (lunch available 12–1 pm)

The class session topics are as follows:

• Day 1: Introduction and Dealing With Structured Data
• Day 2: Dealing With Unstructured Data
• Day 3: LLMs and RAG
• Day 4: Choose Your Own Adventure: Set Up a RAG System on Your Own Data

How to Sign Up

Email the instructor, Sam Corey, for the registration link. You must be a current MIT student (undergraduate or graduate), post-doctoral associate, or researcher to take the class.

Space in this class is limited. You must complete the registration form no later than January 19 (before 5 pm). Registration will close earlier if the class reaches capacity before that date.

Details

Date: Friday, January 23, 2026
Time: 10:30 am–4:45 pm

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Organizer

• Armando Solar-Lezama, Distinguished Professor of Computing

An exploration of how AI is transforming the way we code.

Agenda

10:30–11:15 am

• Armando Solar-Lezama, Distinguished Professor of Computing, MIT

11:15 am–12:00 pm

• Daniel Jackson, Professor of Computer Science, MIT

12:00–1:00 pm | Break for Lunch

1:00–1:45 pm

• Graham Neubig, Associate Professor, Carnegie Mellon University Language Technology Institute; Chief Scientist, Open Hands

1:45–2:30 pm 

• Tim Kraska, Associate Professor of Electrical Engineering and Computer Science, MIT

2:30–3:15 pm   

• Eric Klopfer, Professor and Director, Scheller Teacher Education Program and Education Arcade, MIT

3:15–4:00 pm    

• Varun K. Mohan, Engineer, Google Deepmind; previously Co-Founder & CEO, Windsurf

4:00–4:45 pm | Panel discussion and Q&A

Details

Date: Monday, January 26, 2026
Time: 10:00 am–3:15 pm

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Organizer

• Jim DiCarlo, Director, MIT Siegel Family Quest for Intelligence; Peter de Florez Professor, Department of Brain and Cognitive Sciences

Presented by the MIT Siegel Family Quest for Intelligence (SQI), this workshop will examine the evolving relationship between human intelligence and artificial intelligence.

Agenda

10:00–10:15 am | Overview of Quest Platforms

• Jim DiCarlo, Director, MIT Siegel Family Quest for Intelligence; Peter de Florez Professor, Department of Brain and Cognitive Sciences, MIT

10:15 am–12:00 pm | Cognitive Modeling Approaches

• Josh Tenenbaum, SQI Director of Science; Professor, Department of Brain and Cognitive Sciences, MIT

12:00–1:00 pm | Break for Lunch

1:00–1:15 pm | Model-based and Model-free approaches to Embodied Intelligence

• Leslie Kaelbling, SQI Director of Research; Panasonic Professor, Department of Electrical Engineering and Computer Science, MIT

1:15–1:30pm | Behavioral Data Collection

1:30–2:15 pm | Running Behavioral Experiments With Nodekit

• Michael Lee, SQI Intelligence Observatory Research Scientist

2:15–2:30 pm | Data/Model Comparison

2:30–3:15 pm | Introduction to Brain-Score

• Kartik Pradeepan, SQI Brain-Score Research Scientist

Details

Date: Friday, January 30, 2026
Time: 8:00 am–5:00 pm

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Organizers

• Sam Madden, Faculty Head of Computer Science, Department of Electrical Engineering and Computer Science (EECS); College of Computing Distinguished Professor, MIT
• Eric Klopfer, Professor and Director, Scheller Teacher Education Program and Education Arcade. MIT

Gain a deeper understanding of the applications and impact of AI in education.

Agenda

8:00–9:00 am | Breakfast & Gather

9:00–9:10 am | Welcome

• Sam Madden, Faculty Head of Computer Science, EECS; College of Computing Distinguished Professor, MIT
• Eric Klopfer, Professor and Director, Scheller Teacher Education Program and Education Arcade, MIT

9:10–10:10 am | AI as TA: Teaching With AI in Intro to Machine Learning

• Shen Shen, Lecturer, EECS, MIT
  

10:10–10:40 am | Use of AI in MIT Language Programs

• Per Urlaub, Director, Global Languages; Professor of the Practice of German and Second Language Studies, MIT

10:40–11:00 am | Break

11:00–11:30 am | Use of AI in the MIT Writing Program

• Michael Trice, Lecturer, Writing, Rhetoric and Professional Communication, MIT
  

11:30–12:00 pm | Experiences With AI in the Software Engineering Capstone

• Daniel Jackson, Professor of Computer Science, MIT

12:00–12:45 pm | Break for Lunch

12:45–2:00 pm | Panel: How Does AI Affect How We Learn?

• Eric Klopfer, Professor and Director, Scheller Teacher Education Program and Education Arcade, MIT
• Eric So, Sloan Distinguished Professor of Global Economics and Behavioral Science, MIT
• Antonio Torralba, Faculty Head of Artificial Intelligence and Decision-Making, EECS; Delta Electronics Professor, MIT
• Melissa Webster, Senior Lecturer, Managerial Communication, MIT Sloan School of Management

2:00–2:15 pm | Break

2:15–2:45 pm | Navigating Teaching and Assessing Student Learning in the GenAI Era

• Lourdes Aleman, Associate Director, MIT Teaching + Learning Lab

2:45–3:45 pm | Breakout Sessions

3:45–4:00 pm | Break

4:00–5:00 pm | Closing Discussion